ASSESSING OZONE AND FINE PARTICULATE MATTER
CONCENTRATIONS AND TRENDS IN ONTARIO, CANADA, 2003 – 2012
by
Cameron Hare
A Major Research Paper
presented to Ryerson University
in partial fulfillment of the
requirements for the degree of
Masters of Spatial Analysis (MSA)
Toronto, Ontario, Canada
© Cameron Hare 2013
Author’s Declaration for Electronic Submission of a MRP
I hereby declare that I am the sole author of this MRP. This is a true copy of the MRP,
including any required final revisions.
I authorize Ryerson University to lend this MRP by photocopying of by other means, in
total or in part, at the request of other institutions or individuals for the purpose of
scholarly research.
I understand that my MRP may be made electronically available to the public.
ii
Abstract
Ambient air concentrations of ozone (O3) and fine particulate matter (PM2.5) in southern
Ontario were analyzed in this study. Ontario is Canada’s most populated province and a
large area in southern Ontario shares a border with the United States of America. The
2003 Canada-U.S. Border Air Quality Strategy outlines an initiative to reduce air
pollution, specifically targeting southern Ontario due to its proximity to the U.S. and its
historical air pollution levels. The data were obtained from the Ontario Ministry of the
Environment (MOE) website. The Air Quality Index (AQI) network consists of 40
stations across Ontario that monitor concentrations of up to six pollutants on an hourly
basis. The purpose of the study was to examine ambient air quality trends from 2003 to
2012 by generating prediction surfaces using the ordinary kriging spatial interpolation
technique. Average O3 and PM2.5 levels for each year as well as maximum pollutant
concentrations for the lowest and the highest year for each contaminant were produced.
Ozone is created when volatile organic compounds (VOCs) and nitrogen oxides (NOxs)
react in sunlight.
Fine particulate matter is primarily released by transportation,
residential and industrial processes and can cause severe cardiopulmonary damage and
has been attributed to the development of diabetes. The results show that average ozone
levels increased since 2003, while average fine particulate matter levels decreased. Also,
the maximum concentrations per year for both contaminants decreased significantly.
This indicates that ozone is a continuing problem for Ontario, but fine particulate matter
has been greatly reduced and air quality has generally improved.
iii
Acknowledgements
I would like to thank my supervisor Dr. K. Wayne Forsythe for his guidance and
expertise in aiding me to complete this major research paper.
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Table of Contents
Author’s Declaration for Electronic Submission of a MRP ............................................... ii
Abstract .............................................................................................................................. iii
Acknowledgements ............................................................................................................ iv
Table of Contents ................................................................................................................ v
List of Figures .................................................................................................................. viii
List of Acronyms ............................................................................................................... ix
CHAPTER 1: Introduction ................................................................................................. 1
1.1 Ozone (O3) and Fine Particulate Matter (PM2.5) ....................................................... 2
1.2 Ordinary Kriging Spatial Interpolation ..................................................................... 3
1.3 Research Objectives .................................................................................................. 3
1.4 Major Research Paper Structure ................................................................................ 4
CHAPTER 2: Literature Review ........................................................................................ 5
2.1 Air Pollution in Ontario............................................................................................. 5
2.2 Trans-Boundary Air Pollution ................................................................................... 6
2.3 Ground Level Ozone (O3) ......................................................................................... 7
2.4 Fine Particulate Matter (PM2.5) ................................................................................. 8
2.5 Kriging ...................................................................................................................... 9
2.6 Pollutant Standards.................................................................................................. 10
CHAPTER 3: Manuscript ................................................................................................. 13
3.1 Abstract ................................................................................................................... 13
3.2 Introduction ............................................................................................................. 14
Ozone (O3) ................................................................................................................. 14
Fine Particulate Matter (PM2.5).................................................................................. 15
Canada-Wide Standards (CWSs) and Ontario’s Ambient Air Quality Criteria
(AAQC) ..................................................................................................................... 16
Study Area ................................................................................................................. 16
3.3 Data Collection........................................................................................................ 17
3.4 Methods ................................................................................................................... 18
3.5 Results ..................................................................................................................... 20
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Ozone (O3) ................................................................................................................. 20
Fine Particulate Matter (PM2.5).................................................................................. 34
3.6 Discussion ............................................................................................................... 48
Ozone ......................................................................................................................... 48
Fine Particulate Matter .............................................................................................. 49
3.7 Conclusion............................................................................................................... 52
CHAPTER 4: Recommendations and Future Research.................................................... 53
REFERENCES ................................................................................................................. 55
vi
List of Tables
Table 3.5.1. Ordinary kriging average ozone statistics ..................................................... 22
Table 3.5.2. Ordinary kriging maximum ozone statistics ................................................. 32
Table 3.5.3. Ordinary kriging average fine particulate matter statistics ........................... 37
Table 3.5.4. Ordinary kriging maximum fine particulate matter statistics ....................... 46
vii
List of Figures
Figure 3.2.1. The ambient air quality monitoring network consists of 40 Air Quality
Index (AQI) stations across the province.......................................................................... 17
Figure 3.5.1. Maximum ozone levels................................................................................ 20
Figure 3.5.2. Average ozone levels ................................................................................... 21
Figure 3.5.3. Average hours exceeding the Ontario AAQC ............................................. 22
Figure 3.5.4. 2003 ordinary kriging ozone concentrations ............................................... 23
Figure 3.5.5. 2004 ordinary kriging ozone concentrations ............................................... 24
Figure 3.5.6. 2005 ordinary kriging ozone concentrations ............................................... 25
Figure 3.5.7. 2006 ordinary kriging ozone concentrations ............................................... 26
Figure 3.5.8. 2007 ordinary kriging ozone concentrations ............................................... 27
Figure 3.5.9. 2008 ordinary kriging ozone concentrations ............................................... 28
Figure 3.5.10. 2009 ordinary kriging ozone concentrations ............................................. 29
Figure 3.5.11. 2010 ordinary kriging ozone concentrations ............................................. 30
Figure 3.5.12. 2011 ordinary kriging ozone concentrations ............................................. 31
Figure 3.5.13. 2012 ordinary kriging ozone concentrations ............................................. 32
Figure 3.5.14. 2003 ordinary kriging maximum ozone concentrations ............................ 33
Figure 3.5.15. 2011 ordinary kriging maximum ozone concentrations ............................ 34
Figure 3.5.16. Maximum fine particulate matter levels .................................................... 35
Figure 3.5.17. Average fine particulate matter levels ....................................................... 35
Figure 3.5.18. Average days exceeding the CWS ............................................................ 36
Figure 3.5.19. 2003 ordinary kriging fine particulate matter concentrations ................... 37
Figure 3.5.20. 2004 ordinary kriging fine particulate matter concentrations ................... 38
Figure 3.5.21. 2005 ordinary kriging fine particulate matter concentrations ................... 39
Figure 3.5.22. 2006 ordinary kriging fine particulate matter concentrations ................... 40
Figure 3.5.23. 2007 ordinary kriging fine particulate matter concentrations ................... 41
Figure 3.5.24. 2008 ordinary kriging fine particulate matter concentrations ................... 42
Figure 3.5.25. 2009 ordinary kriging fine particulate matter concentrations ................... 43
Figure 3.5.26. 2010 ordinary kriging fine particulate matter concentrations ................... 44
Figure 3.5.27. 2011 ordinary kriging fine particulate matter concentrations ................... 45
Figure 3.5.28. 2012 ordinary kriging fine particulate matter concentrations ................... 46
Figure 3.5.29. 2005 ordinary kriging maximum fine particulate matter concentrations .. 47
Figure 3.5.30. 2012 ordinary kriging maximum fine particulate matter concentrations .. 48
viii
List of Acronyms
µg/m3 - Micrograms per Cubic Metre
µm - Microns
AAQC - Ambient Air Quality Criteria
AQI - Air Quality Index
ASE - Average Standard Error
CCME - Canadian Council of Ministers of the Environment
CWS - Canada-Wide Standard
LOAEL - Lowest Observed Adverse Effect Level
MOE - Ontario Ministry of the Environment
NOx - Nitrogen Oxides
NOAEL - No Observed Adverse Effects Level
O3 - Ground-Level Ozone
PEL - Probable Effect Level
PM - Particulate Matter
PM2.5 - Fine Particulate Matter (with a diameter of 2.5 µm or less)
ppb - parts per billion
RMSPE - Root-Mean Square Prediction Error
SRMSPE - Standardized Root-Mean Square Prediction Error
TEL - Threshold Effect Level
VOC - Volatile Organic Compound
WHO - World Health Organization
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CHAPTER 1: Introduction
Air pollution is an issue of contention at all levels of government, all geographic scales,
and has stakeholders in countless sectors of society. Poor air quality not only has
negative effects on human health, but, as outlined by Ontario’s Ambient Air Quality
Criteria (AAQC), can have negative effects on odour, vegetation, soiling, visibility, or
corrosion, depending on the contaminant (AAQC, 2012). Therefore, it is not a small
scale issue that affects a particular group or sector, but one that spans many areas.
Ontario and more specifically southern Ontario, is dominated by major urban centres
such as Toronto, London, Hamilton, and Windsor. According to 2010 estimates from the
Ontario Ministry of the Environment (MOE), emissions of volatile organic compounds
(VOCs) and nitrogen oxides (NOxs) (the main precursors to ozone) were 36% and 71%
respectively by transportation, and 53% and 21% respectively by industrial processes
(MOE, 2013a). Considering major cities have the most automobile traffic and high levels
of industrial activity, these are areas of concern for air pollution. It is slightly different
for fine particulate matter, as the main emitters are residential (39%), transportation
(24%) and industrial processes (30%).
Another reason southern Ontario is an area of concern for air pollution is its proximity to
the United States border. According to Miller et al. (2010), over half of the air coming
into Windsor, Ontario was from the United States over 48 hours, while that number
climbed to over 80% when examining some 24 hour trajectories. In accordance with this
reality, the Canadian Council of Ministers of the Environment (CCME) has adopted
1
regulations and provisions concerning trans-boundary air pollution, as well as creating
goals to be met by both sides (CCME, 2012).
One of the major concerns in Ontario is the level of smog.
It results from the
combination of multiple contaminants and meteorological factors, and can lead to
reduced visibility and adverse health effects in humans and vegetation.
Smog has
become such a major issue in Ontario that the MOE has developed a smog alert network,
informing the province when it is bad enough to cause serious health problems (MOE,
2013c), and some areas such as Windsor have provided free public transit on days when
the smog reaches dangerous levels (City of Windsor, 2003). Two of the main ingredients
of smog are ground-level ozone and fine particulate matter.
1.1 Ozone (O3) and Fine Particulate Matter (PM2.5)
Both ozone and fine particulate matter are recognized as hazards to human health. There
are of particular concern because they are the main contributors to the level of smog at
any given time. Ozone is produced when VOCs and NOxs react in sunlight, and is
therefore most prominent on sunny summer days (MOE, 2013a). Ozone is not produced
directly like other chemicals, and is dependent on precursors for its creation. Fine
particulate matter, is produced directly, most commonly as a result of anthropogenic
activity (93% is emitted by residential, transportation and industrial sectors) (MOE,
2013a). As explained previously, both pollutants react to form smog, a concern that is of
high importance to the Province of Ontario.
2
1.2 Ordinary Kriging Spatial Interpolation
Ordinary kriging is a spatial interpolation technique that allows for the prediction of
values at unsampled locations. It produces a continuous coverage surface for an area
based on observations at known points. Ordinary kriging possesses an advantage over
other spatial interpolation techniques in that it can be statistically validated (Gawedzki
and Forsythe, 2012). Another advantage of kriging is that it does not assume a normal
distribution of the data. However, if the data are normally distributed, kriging is the best
unbiased predictor (Negreiros et al., 2010). The MOE AQI network measures air quality
and pollutant concentrations at 40 points across the province, so the use of ordinary
kriging would allow for the creation of a prediction surface that facilitates better
interpretation of air quality trends.
1.3 Research Objectives
The research objectives for this paper can be divided into four sections:
1. To assess concentrations of ozone and fine particulate matter in ambient air in
Ontario from 2003 to 2012.
2. To quantify the change that has occurred in that time frame.
3. To examine contaminant levels and compare them to the Canada-Wide Standard
(CWS) for fine particulate matter and the AAQC for ozone for each year, and
determine trends during the time period.
4. To assess the effectiveness of ordinary kriging for generating ambient pollutant
concentrations.
3
1.4 Major Research Paper Structure
The structure of this paper is presented in a manuscript format. The first chapter provides
a general introduction to the research and the objectives of the paper. Chapter 2 presents a
literature review pertinent to the scope of the research project. Chapter 3 is organised as a
standalone manuscript. It consists of typical journal sections including: abstract,
introduction, data collection, methodology, results, discussion, and conclusion. Chapter 4
concludes the paper, briefly outlining recommendations for future research.
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CHAPTER 2: Literature Review
2.1 Air Pollution in Ontario
Air pollution is a subject of concern that has been around for many years, but has
increased in importance with other societal changes, the most notable of which is
population growth. The drastic increase in population in past decades has seen several
parallels, such as growth in population density, industry, and the number of automobiles
on the road, all of which contribute to the reduction of air quality. As can be predicted,
areas that have the highest population density can have the highest levels of industrial
activity and the highest number of automobiles, and are consequently areas of concern for
air quality and ambient pollutant levels (Kantor et al., 2010). As noted by Khan et al.
(2010), the deterioration of urban air quality is causing increasing concern, and can have
detrimental effects on human health.
Ontario is Canada’s most populous province, and home to some of its largest cities. The
Ontario Ministry of the Environment (MOE) monitors ambient air pollution by taking
measurements of pollutant levels on an hourly basis at 40 stations province wide. Each
station measures up to six pollutants, including ozone, fine particulate matter, nitrogen
dioxide, carbon monoxide, sulphur dioxide, and total reduced sulphur compounds. As a
result of its population, and concurrent level of industry and automobile use, southern
Ontario has the highest levels of air pollution in Canada (Miller and Watmough, 2009).
Cities have the largest impacts on air pollution and the highest levels of air pollution due
to their natural concentration of humans, materials and activities (Fenger, 1999).
5
The particular health effects of ozone and fine particulate matter will be examined in
subsequent sections, but in Toronto, general air pollution has been the cause of serious
health effects. In a study by Kantor et al. (2010), province-wide emissions for Ontario
were studied, as well as urban air pollution in the City of Toronto. The authors focused
on Ontario and Toronto because the large population would involve a great number of
individuals being affected. In addition, the study area was chosen because of increased
concern due to the estimated fatalities resulting from air pollution. According to the
research, approximately 1,700 deaths per year are a direct result of urban air pollution,
and 5,800 deaths occur throughout the Province of Ontario (Kantor et al., 2010).
Not only is Ontario adversely impacted by air pollution in regards to human and
environmental health, but in a financial aspect as well. It has been found that the impacts
of fine particulate matter and ozone total approximately 9.6 billion dollars in health and
environmental damages every year (Tian and Chen, 2007). The authors also found that
areas of Southern Ontario exhibit the highest levels of acidic deposition, ozone, fine
particulate matter and hazardous pollutants in eastern Canada.
2.2 Trans-Boundary Air Pollution
Unfortunately, despite the acts and measures in place by the Ontario Ministry of the
Environment and Environment Canada to improve air quality and reduce pollutant
concentrations, the source of a large percentage of air pollution is not Ontario, but the
United States.
Especially in south-western Ontario, trans-boundary air pollution
contributes to lower air quality due to the proximity of the border. Miller et al. (2010)
examined trans-boundary pollution in Windsor, Ontario. It was found that 48 hour back
trajectories showed that 53 to 55 percent of air masses arriving in the city were from the
6
United States, while examining the 24 hour back trajectories showed that 81 to 82 percent
of air originated across the border. The difference was due to air patterns, and how the
source of air masses differs when comparing back trajectories spanning one day versus
two days. The study was conducted for two years, 2008 and 2009, and little inter-annual
variability was found (Miller et al., 2010). In another study by Galvez (2007), a model
was created to examine trans-boundary air pollution in southern Ontario using 72 hour
back trajectories.
He found that approximately 60 percent of ozone is due to
anthropogenic emission release from the United States during smog episodes. Therefore
it is difficult to regulate air pollution from within Ontario as trans-boundary pollution
accounts for more than half of the air pollution in the province.
2.3 Ground Level Ozone (O3)
Ozone (O3) has been identified as one of the pollutants of concern when dealing with air
pollution, and is a major component associated with smog. Ozone is created when
nitrogen oxides (NOX) and volatile organic compounds (VOCs) interact in sunlight
(Ozbay et al., 2011). Ozone is not emitted directly into the atmosphere, but relies on
other meteorological factors and chemical emissions for its formation (MOE, 2013a).
For example, when detailing the levels of ozone, the MOE does not show the sources of
ozone itself, but the major emissions by sector of VOCs and NOxs, as ozone is created
with these two contaminants as precursors. Since ozone does not only require VOCs and
NOxs to form but sunlight and heat as well, high levels of ground-level ozone have
generally been found in the summer months (MOE, 2013a). Despite cities having the
highest levels of air pollution, they are generally found to have the lowest average ozone
7
levels. This is because when ozone reacts with NOx, levels of which are generally
highest in large cities, overall ozone levels are reduced (MOE, 2013a).
Ozone has been known as a human and environmental concern for many years, and has
thus been studied extensively in the literature.
Joseph et al. (2013) recognize the
extensive research of the health effects of ozone, and agree that estimation of pollutant
levels, though difficult due to the usual sparse distribution within air monitoring
networks, is important and necessary for planning and policy making. The authors
applied different spatial interpolation techniques (simple averaging, nearest neighbour,
inverse distance weighting, ordinary kriging, and universal kriging) on two datasets:
Houston, Texas (up to 42 air monitoring stations) and Los Angeles, California (up to 27
air monitoring stations) using the three highest maximum eight hour ozone
concentrations for each area. It was found that ordinary kriging yielded superior results,
along with reliable confidence intervals (Joseph et al., 2013). Hooyberghs et al. (2006)
also used spatial interpolation to attempt to quantify ozone concentrations in Belgium.
Comparing inverse distance weighting and ordinary kriging, the authors also found that
ordinary kriging produced superior results, outperforming the other models.
2.4 Fine Particulate Matter (PM2.5)
Fine particulate matter is of particular concern due to its ability to be inhaled deeply into
the lungs (Khan et al., 2010). The naming of PM2.5 derives from the particle size in the
air; fine particulate matter has particles with a diameter of 2.5 microns (µm) or less.
When inhaled, the particles can cause or aggravate serious health issues, such as
cardiopulmonary and lung cancer, as well as death; Pope et al. (2002) found that an
increase of 10 µg/m3 in PM2.5 concentrations resulted in an approximate 6% increase in
8
cardiopulmonary mortality and an 8% increase in lung cancer mortality. Additionally, in
a study by Chen et al. (2013), based on the hypothesis that fine particulate matter can
induce insulin resistance, the risk of diabetes in relation to long term exposure to fine
particulate matter was examined. Based on the findings, the authors suggest that long
term exposure can contribute to the development of diabetes (Chen et al., 2013). Kantor
et al. (2010) also note that PM2.5 is generally considered to be the greatest risk to human
health due to its ability to enter the lungs and cause irritation and further damage.
Yu (2010) recognizes the importance of spatial information of these pollutants for
epidemiological research to further understand the health effects of fine particulate
matter. The author used principal component analysis (PCA) to examine PM2.5 trends in
Taiwan, utilizing daily mean concentrations from 2006 to 2008. The findings indicated
that concentrations were largely the highest in winter and the lowest in summer. Lee et
al. (2012) examined PM2.5 concentrations across the continental United States using a
combination of remote sensing and a geostatistical kriging model. The authors used
monthly pollutant concentration averages as their aggregations, and found that within
approximately 100 metres of a monitoring station, kriging outperformed their remote
sensing methods.
2.5 Kriging
Kriging is a method of spatial interpolation that generates an estimated prediction surface
based on measurements at discrete points. Therefore, it is a valuable technique especially
when dealing with environmental data, as they are almost always measured at discrete
locations (Kumar et al., 2007) and cannot feasibly be measured continuously. This is
9
advantageous because kriging allows for the prediction of data values at unsampled
locations (Shad et al., 2009).
Different methods of kriging have been used in the literature to assess discrete
environmental data and create prediction surfaces. Forsythe et al. (2010) used ordinary
kriging to interpolate zinc concentrations in Lake Ontario, and compared the prediction
surfaces generated for historical and contemporary data to assess change that had
occurred.
Beelen et al. (2009) compared ordinary kriging, universal kriging, and
regression models to predict air pollution across the European Union, and mention that
both kriging techniques have been previously used to successfully model ozone and
particulates at the local scale.
From the results, the authors note that the kriging
techniques performed substantially better when measuring PM10, and ordinary kriging
performed better than the regression at an urban scale. In the literature, it was found that
ordinary kriging is a superior method for predicting ambient air pollution concentrations
(Beelen et al., 2009; Hooyberghs et al., 2006; Joseph et al., 2013).
2.6 Pollutant Standards
In response to the increasing issue of air pollution, emissions standards have been
developed in many countries and specific pollutant concentration goals have been set
(Barton, 2008; WHO, 2006). However, since there is variation not simply at a global
level, but at a regional and even local level, it is difficult to apply universal standards to
any particular place. In 2006, the World Health Organization (WHO) published an
update detailing air quality guidelines (WHO, 2006). For PM2.5, a standard of 10 µg/m3
was set as an annual mean, with 25 µg/m3 as a 24 hour mean. For ozone, the standard
was set at 100 µg/m3 for an eight hour mean (WHO, 2006). In Canada, the Canadian
10
Council of Ministers and Environment (CCME), a joint framework involving federal,
provincial, and territorial governments, are responsible for taking action on many of the
modern air pollution problems being faced (Barton, 2008). In June of 2000, this group
signed the Canada-wide Standards (CWSs) for two particularly dangerous pollutants:
Particulate Matter (PM) and Ozone (O3). The goal was to commit governments to
significantly reduce the concentrations of these pollutants, as they have been found to be
the main ingredients of smog and produce serious health effects on human populations.
The CWS for PM is focused on fine particulate matter, or PM2.5, and is expected to be 30
µg/m3 averaged over 24 hours, while the CWS for ozone is expected to be 65 ppb
averaged over eight hours (Barton, 2008).
Other standards have been developed for toxic chemicals and contaminants, and
extensive research was done to obtain levels for ozone and fine particulate matter. The
CCME has developed the Threshold Effect Level (TEL) and the Probable Effect Level
(PEL) for many contaminants. The TEL is the level of contamination concentrations
below which adverse biological effects are expected to occur rarely, while the PEL is the
level of contamination concentrations above which adverse biological effects are
expected to occur frequently (Gawedzki and Forsythe, 2012). Other measures that have
been developed for some toxic chemicals include the No Observed Adverse Effects Level
(NOAEL) and the Lowest Observed Adverse Effect Level (LOAEL). The NOAEL
represents the highest level of the substance below which there are no adverse effects,
while the LOAEL represents the lowest level at which there are observed adverse effects.
However, there is no threshold for the health effects of ozone and particulate matter, and
the above measures have not been found useful for setting standards (NEPC, 2010).
11
In addition to the CWSs, the Province of Ontario has another set of standards known as
the Ambient Air Quality Criteria (AAQC). Developed by the MOE, the AAQC presents
desirable concentrations of over 300 ambient air pollutants based on factors such as
health, odour, vegetation, soiling, visibility, corrosion and others (AAQC, 2012). The
AAQC, since it was developed at the provincial level, is thus a better set of guidelines to
follow. The AAQC for ozone, unlike the CWS, is measured over one hour rather than
eight, and is set at 80 parts per billion (ppb), rather than 65. There is no AAQC for fine
particulate matter, as the MOE follows the CWS.
The AAQC for ozone and the CWS
for fine particulate matter were used in this study.
The AAQC for ozone and the CWS for fine particulate matter have been used in the
literature, albeit not extensively. Geddes et al. (2009) conducted a study to examine
nitrogen oxides, VOCs and ozone in Toronto, Ontario, Canada. The authors reference
the CWSs for ozone and mention the provision of trans-boundary pollution put forward
by the CCME, and how this is affecting Toronto and other areas of Ontario near the
United States border. However, the MOE Air Quality Report uses these measures as
benchmarks to determine the state of air quality in any particular year.
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CHAPTER 3: Manuscript
USING ORDINARY KRIGING TO EXAMINE AMBIENT CONCENTRATIONS
OF OZONE (O3) AND FINE PARTICULATE MATTER (PM2.5) IN ONTARIO,
CANADA, 2003 – 2012
3.1 Abstract
Ambient concentrations of ozone (O3) and fine particulate matter (PM2.5) were analyzed
for the period of 2003 to 2012 in Ontario, Canada. Ontario is Canada’s most populous
province, and consequently has the highest levels of automobile use and industrial
activity, both of which play a major role in the level of air pollution. The southwestern
part of Ontario is very urban, containing large population centres such as Toronto,
Hamilton, London and Windsor. It has been recognized that urban areas produce the
most air pollution, and will be more adversely affected by poor air quality. The data were
collected by the Ontario Ministry of the Environment (MOE) Air Quality Index (AQI)
monitoring network stations. Pollutant concentrations were collected on an hourly basis
from 2003 to 2012. The purpose of this study was to analyze the concentration trends of
each pollutant over the time period, and to use spatial interpolation techniques to generate
spatial prediction surfaces for the province. Levels of ozone and fine particulate matter
were then compared to the Canada-Wide Standard (CWS) for fine particulate matter and
the Ambient Air Quality Criteria (AAQC) for ozone to determine trends. This could be
used to facilitate environmental decision making and be corroborated with existing MOE
air quality research. Ozone and fine particulate matter are the two main ingredients of
smog, and both can have serious effects on human and vegetative health, odour,
visibility, soiling and corrosion.
Keywords: Kriging, Ontario, Air Quality, Air Pollution, Ozone, Fine Particulate Matter
13
3.2 Introduction
Ontario is Canada’s second largest province by area, and largest by population. It
contains some of Canada’s largest and most urban areas, such as Toronto, Windsor,
Ottawa, and Hamilton. This also means that Ontario has the highest automobile use and
some of the heaviest industrial activity in Canada. As Geddes et al. (2009) point out,
vehicle registration in Ontario increased by 700,000 between 2000 and 2007). According
to the Ontario Ministry of the Environment Air Quality Report 2011 (MOE, 2013a),
automobile traffic is one of the main causes of air pollution; it accounts for the release of
approximately 36 percent of volatile organic compounds (VOCs), 71 percent of nitrogen
oxides, and 24 percent of fine particulate matter. As a result, it has been found that
southern Ontario has the highest levels of air pollution in Canada (Miller and Watmough,
2009).
There are two contaminants that are especially concerning: ozone and fine particulate
matter. They are the main ingredients of smog, a factor of utmost importance when
considering air quality in Ontario, and both ozone and fine particulate matter have been
found to cause serious health effects. Therefore to provide a detailed spatial analysis of
each contaminant since 2003 would be greatly beneficial in understanding air quality
trends and providing information to policy makers in Ontario.
Ozone (O3)
Ground-level ozone is a contaminant of concern for the Province of Ontario. Naturally
occurring stratospheric level ozone is beneficial to the environment, as it shields the Earth
from ultraviolet radiation, but formation at ground level causes risks to human and
14
environmental health alike. Ozone is formed when VOCs and nitrogen oxides (NOxs)
react in direct sunlight, and is therefore most prominent on sunny summer days. Ozone
has been recognized by the Ontario AAQC as a threat to human health, and desirable
levels have been determined (AAQC, 2012). Tropospheric ozone, as it is also known, is
a major concern for southern Ontario due to its effects on respiratory health and
agricultural crops, along with its role in the formation of smog (Galvez, 2007).
Fine Particulate Matter (PM2.5)
Fine particulate matter, the other main ingredient in the formation of smog, has a
diameter of 2.5 µm or less. This classification is important, as the size of the particles
relates to how dangerous the particulate matter is. Particulate matter (PM) has been
studied extensively in the literature (Chen et al., 2013), and PM2.5 is receiving more focus
as the smaller particle size makes it easier for the contaminant to enter the lungs and
damage health. According to Sharma and Maloo (2005), finer particulate matter has the
strongest health effects.
Williams (2008) also cites the World Health Organization
(WHO) in concluding that there is now more reason to suggest that PM2.5 is of greater
concern than PM10 due to its fine fraction. Amatullah et al. (2012) studied different sizes
of particulate matter and how this variability can have varying health effects on a human.
They found that coarse (PM2.5-10), fine (PM0.15-2.5), and ultrafine (PM0.2) particulate matter
all adversely affect lung function and airway responsiveness. Also, it was found that
coarse and fine particulate matter (such as present in Ontario) does most of the damage to
health at the site where it is deposited in the airways (Amatullah et al., 2012).
15
Canada-Wide Standards (CWSs) and Ontario’s Ambient Air Quality Criteria (AAQC)
Canada-Wide Standards (CWSs) were developed for many contaminants by the Canadian
Council of Ministers of the Environment (CCME) in 2000 (CCME, 2013).
These
standards were developed to publish acceptable levels of the various contaminants, and
also as goals for Canada as a whole to reach. For ozone, the CWS, expressed as an eight
hour average, is 65 ppb, and for fine particulate matter, expressed as a 24 hour average, is
30 µg/m3. However, there are also standards developed at the provincial level. The
Ontario Ambient Air Quality Criteria (AAQC) details the desired levels of contaminants
in ambient air in Ontario, and standards exist for over 300 contaminants. Among these is
ozone, which, differing from the CWS for ozone, has an AAQC of 80 parts per billion
(ppb) for one hour averages (AAQC, 2012). There is no AAQC for fine particulate
matter, and Ontario uses the CWS as its standard.
Study Area
Ontario is Canada’s largest province, and subsequently contains some of its largest and
most industrial cities. Cities such as Toronto and Ottawa represent large populations,
while others such as Windsor and Hamilton represent major industrial centres. Both of
these factors are major considerations when examining air quality and pollution.
Since 2003, Ontario has experienced major population growth, along with an increase in
automobile numbers and a growing industrial sector, all of which have an impact on air
quality. According to the 2011 Ontario Air Quality Report, a large portion of air pollution
can be attributed to automobiles and industrial activities (MOE, 2013a).
16
The study focuses on southern Ontario, as the Air Quality Index (AQI) station network of
the MOE (see the next section) covers this area most extensively, and does not have any
stations in the far northern areas of Ontario. Also, southern Ontario has a much higher
population, automobile count, industrial activity and closer proximity to the United States
border than the north, so it is of more concern when examining air pollution. Figure 3.2.1
shows the distribution of AQI stations throughout southern Ontario.
Figure 3.2.1. The ambient air quality monitoring network consists of 40 Air Quality
Index (AQI) stations across the province
3.3 Data Collection
The data for this study were collected by the Ontario Ministry of the Environment from
2003 until 2012. Each AQI station measures hourly ambient concentrations of up to six
pollutants, including ozone, fine particulate matter, nitrogen dioxide, carbon monoxide,
sulphur dioxide, and total reduced sulphur compounds. Data for ozone and fine
particulate matter were collected hourly at every station, from 2003 to 2012.
Since hourly data exist for every day in the 10 year period, they were aggregated before
analysis based on the literature (Lee et al., 2012; Miller et al., 2010; MOE, 2013a), as
17
well as the CWSs for ozone and fine particulate matter. Both the WHO and the CCME
recognized the same time frame for each pollutant; ozone was measured as an average
over eight hours, while fine particulate matter was measured as an average over 24 hours.
Also, to allow for more accurate surface predictions using ordinary kriging, the Thunder
Bay AQI station was omitted from analysis. Due to the remote nature of this station, the
prediction surface between it and the other AQI stations would not be accurate and the
results would be skewed. Therefore, only 39 of the 40 stations were examined in this
analysis, focusing on southern Ontario.
Only ozone and fine particulate matter that are measured at all of the AQI stations. The
next most measured pollutant was nitrogen oxides (NOx), which was measured at 36 of
the 40 stations, while the other pollutants were measured sparsely throughout the AQI
network. To include all of the pollutants would result in an incomplete and therefore
inaccurate spatial analysis, so only the two pollutants with complete coverage were
examined.
3.4 Methods
An ordinary kriging spatial interpolation method was used to assess air pollution in
Ontario. One of the advantages of ordinary kriging over other spatial interpolation
methods is the ability to statistically validate the prediction surface generated (Gawedzki
and Forsythe, 2012).
There are several statistics generated when the analysis is
performed that can be compared to ideal numbers so the validity of the model can be
judged. These statistics include the Mean Prediction Error (MPE), the Root-Mean Square
Prediction Error (RMSPE), the Average Standard Error (ASE), and the Standardized
Root-Mean Square Prediction Error (SRMSPE).
18
To achieve acceptable results, the
statistics should be the following: the MPE should be as close to 0 as possible, the ASE
should be as small as possible and below 20, and the SRMSPE should be as close to 1 as
possible. If the SRMSPE is greater than 1, there is an underestimation of the variability
of predictions; if it is less than 1, there is an overestimation of the variability (Forsythe
and Marvin, 2009). Additionally, three models (Spherical, Exponential, and Gaussian)
were compared based on previous ordinary kriging studies (Forsythe et al., 2010;
Gawedzki and Forsythe, 2012) to determine which produced the best output statistics.
These statistics and models were adhered to when choosing the best parameters for
ordinary kriging.
For ozone, the maximum one hour average and the mean one hour average were applied
to each station for each year. For fine particulate matter, the maximum 24 hour average
and the mean 24 hour average were applied to each station for each year. These levels
are both based on methods found in the literature (Hooyberghs, 2006; MOE, 2013a), as
well as the CCME CWSs for fine particulate matter and the AAQC for ozone. To
examine the distribution of contamination levels, the number of averages (one hour for
ozone and 24 hours for fine particulate matter) that fell above and below the CWS and
AAQC for each respective contaminant were examined as well. This gives good insight
into how Ontario is exceeding, meeting, or falling below the CWSs for each year.
Python programming language scripts were written to parse the relevant data to apply to
each station. The raw data came in a table format with hourly contaminant readings for
each hour, day, station, and year. For ozone, the script pulled out the highest reading for
each station year, as well as getting an average of all the values. For fine particulate
19
matter, the script returned the highest 24 hour average for each station from each year, as
well as finding the average of all the 24 hour readings for each.
3.5 Results
Ozone (O3)
Ozone is created from the interaction between sunlight and other chemicals such as
VOCs and NOxs. Therefore, it was anticipated that ground-level ozone levels would be
the highest during the summer months as also stated in the Ontario Air Quality Report
2011 (MOE, 2013). This proved true when examining the findings, as nearly all of the
maximum one hour averages were recorded between May and September.
Maximum ozone levels have been found to be fluctuating, but generally have declined
since 2003. From 2003 to 2007, there are peaks and troughs, before dropping from 2008
to 2011. A slight spike was experienced in 2012. Figure 3.5.1 shows these results.
Maximum O3
120
100
ppb
80
60
Trend
40
20
0
2003
2004
2005
2006
2007 2008
Year
Figure 3.5.1. Maximum ozone levels trend
20
2009
2010
2011
2012
By contrast, the findings for the maximum ozone levels, average ozone levels for each
year are steadily increasing. There are peaks and troughs throughout, but a general
increasing trend can be seen. Figure 3.5.2 shows the results.
Average O3
29
28
ppb
27
26
25
Trend
24
23
22
2003
2004
2005
2006
2007 2008
Year
2009
2010
2011
2012
Figure 3.5.2. Average ozone levels trend
The average hours per AQI station exceeding the AAQC mirrors the maximum ozone
levels. Figure 3.5.3, similar to Figure 3.5.1, shows the initial peaks and troughs before a
large decline in 2008, also showing the spike in 2012. According to the MOE (2013c),
2012 appears to be an anomalous year in terms of smog alerts, as it had many more than
previous years, as well as many more than 2013 (to date).
21
Average Hours per Station
Hours Exceeding AAQC
60
50
40
30
Trend
20
10
0
2003
2004
2005
2006
2007 2008
Year
2009
2010
2011
2012
Figure 3.5.3. Average hours exceeding the Ontario AAQC
The ordinary kriging maps produced for average ozone for each year, beginning with
Figure 3.5.4, were then examined. Generally, the trends shown in each map reflect the
patterns shown in Figure 3.5.1. For all of the ordinary kriging prediction surfaces, the
variability of predictions was slightly overestimated, as the Standardized Root-MeanSquare Prediction Error (SRMSPE) was always below 1 (Gawedzki and Forsythe, 2012).
The statistics can be seen in Table 3.5.1.
Table 3.5.1. Ordinary kriging average ozone statistics
Year
Model
MPE
2003
Spherical
0.010413022
2004
Spherical
-0.015705917
2005
Exponential
-0.028425011
2006
Exponential
-0.079917638
2007
Exponential
-0.067776642
2008
Exponential
0.015975435
2009
Exponential
0.029617946
2010
Exponential
0.04826623
2011
Exponential
-0.087178798
2012
Exponential
-0.035973304
22
ASE
3.542624196
3.273941933
3.183712899
2.595975844
2.850451305
2.82920142
2.428636948
2.793951335
2.185501206
2.226937451
SRMSPE
0.99983069
0.948989748
0.944780088
0.993361277
0.973162918
0.946996013
0.956480634
0.947015023
0.971187681
0.970389625
Figure 3.5.4 shows ozone concentrations from 2003. One of the apparent trends is that
concentrations of ozone appear to be the lowest around Toronto and Mississauga, and
increase outward. All of the areas fell between 22 ppb and 30 ppb, with the highest
concentrations appearing on the shores of Lake Huron and Georgian Bay. Since these
represent averages for the year, no areas exceed the AAQC for ozone.
Figure 3.5.4. 2003 ordinary kriging ozone concentrations
Figure 3.5.5 shows that the results for 2004 ozone concentrations are very similar to those
of 2003. When examining Figure 3.5.1, 2004 had a lower overall average than 2003.
Though the increments are the same, a much larger area falls in the lowest prediction
interval, and subsequently, a much smaller area falls into the highest prediction interval.
The areal trends are similar to each other, with the Toronto and Golden Horseshoe area,
along with Ottawa, experiencing the lowest concentrations of ground-level ozone, and the
23
higher concentrations appearing to the north near Georgian Bay. This can be explained
by the reaction of ozone with nitrogen oxides by which ozone levels are actually reduced.
Since NOx levels are the highest in cities, ozone levels would subsequently be the lowest
(MOE, 2013).
Figure 3.5.5. 2004 ordinary kriging ozone concentrations
Figure 3.5.6 again shows similar trends to the previous years. However, the pollution
levels have increased, with the lowest being 24 to less than 26 ppb, and the highest being
30 to less than 32 ppb. These results are in accordance with Figure 3.5.1, which shows
that average ozone levels increased in 2005 and were higher than those in 2003. Despite
the difference in concentration levels, the trend is almost identical to the previous two
years, showing increasing concentrations as distance increases from the larger cities such
as Toronto and Ottawa. As explained for 2004, this can be attributed to the higher levels
of NOxs in large cities, resulting in the reduction of ozone concentrations.
24
Figure 3.5.6. 2005 ordinary kriging ozone concentrations
The similarity in trends continues in 2006 as seen in Figure 3.5.7. Also, since 2005,
ozone levels have gone down, and the prediction intervals have returned as they were for
2003 and 2004. It is the most similar to the 2004 ordinary kriging map, and this can be
expected; according to Figure 3.5.2, both years have almost identical average ozone
levels. Areas west of Toronto towards Windsor, however, now all fall below 28 ppb,
whereas a length of area was predicted to have 28 ppb or higher in 2003.
25
Figure 3.5.7. 2006 ordinary kriging ozone concentrations
The year 2007 shown in Figure 3.5.8 represents another increase in ozone concentrations,
as can be seen in Figure 3.5.1, as well as looking at the prediction intervals, which have
increased to the same levels as 2005. However, the similarity in trends for all years still
exists. The area to the north near Georgian Bay no longer exhibits higher predicted
concentrations, while the area around Kingston shows concentration levels greater than or
equal to 30 ppb.
26
Figure 3.5.8. 2007 ordinary kriging ozone concentrations
Similar trends can be seen in 2008 in Figure 3.5.9, but the range of concentrations
decreased. The lowest prediction intervals are the same as 2007, a peak year, but the
highest prediction intervals are the same as the lower years. The Kingston area again
shows higher ozone concentrations, and the lowest prediction area is again near Toronto
and the surrounding area. More of the total area, however, falls into the second lowest
prediction interval, showing improvement from the 2007 findings.
27
Figure 3.5.9. 2008 ordinary kriging ozone concentrations
The 2009 prediction surface in Figure 3.5.10 shows similarities to 2008 in terms of the
prediction intervals, but overall shows lower concentrations. This parallels the findings
in Figure 3.5.1, indicating another drop in average ozone concentrations. Though the
concentration interval of 28 to less than 30 ppb is present on the map, it is only a
miniscule area on the shore of Lake Huron. Again, the Toronto, Golden Horseshoe and
Ottawa regions show the lowest predicted ozone concentrations, while most of the rest of
the area falls in the 26 to less than 28 ppb category.
28
Figure 3.5.10. 2009 ordinary kriging ozone concentrations
Figure 3.5.11 shows similarities to 2009, with the exception that the ozone prediction
concentrations are all higher.
It also represents the year with the highest ozone
concentrations for the time period. The entire area shows concentrations above 26 ppb,
which had never occurred prior to 2010. There is also a large area to the west along the
shore of Lake Huron that exhibits the highest concentrations of ground-level ozone.
29
Figure 3.5.11. 2010 ordinary kriging ozone concentrations
The results for 2011 in Figure 3.5.12 show a general reduction in ozone levels across the
study area. The Toronto and Ottawa regions were lower than 2010, as well as most of the
surrounding area. The portion of the map that had been 30 ppb or above is now mostly
below 30 ppb. The trend remains similar to all of the study years, with the big cities
showing the lowest concentrations of ground-level ozone.
30
Figure 3.5.12. 2011 ordinary kriging ozone concentrations
Figure 3.5.13 shows an increase from 2011, and indeed has one of the highest ozone level
averages according to Figure 3.5.2. Though similar in trend to 2011, no areas fall under
26 ppb, as in 2010.
The area around Kingston again has higher predicted ozone
concentrations, though the area is not as extreme as in 2010. The area that falls between
30 ppb and under 32 ppb is almost identical to that in 2010.
31
Figure 3.5.13. 2012 ordinary kriging ozone concentrations
Ordinary kriging maps were then produced for the maximum ozone readings from each
station. Two maps were produced: one from the year with the highest maximum readings
(2003) and one for the lowest readings (2011). Both the prediction surfaces slightly
overestimated the variability of predictions with an SRMSPE below 1. The statistics can
be seen in Table 3.5.2.
Table 3.5.2. Ordinary kriging maximum ozone statistics
Year
Model
MPE
ASE
2003
Spherical
0.25476527
11.70531671
2011
Spherical
0.137377466
7.108358458
SRMSPE
0.982511633
0.973711537
Maximum ozone levels in 2003, shown in Figure 3.5.14, all exceeded the AAQC for
ozone. Every part of the interpolation surface has concentrations of at least 95 ppb, with
areas around Kingston and Windsor reaching between 115 and 120 ppb.
32
Figure 3.5.14. 2003 ordinary kriging maximum ozone concentrations
Figure 3.5.15 shows a drastic reduction in maximum ozone levels. Nearly the whole area
has concentrations below the lowest area in 2003. Only a small area falls above 100 ppb,
an area which had concentrations over 110 ppb in 2003. The AAQC of 80 ppb was
represented as an isoline on the map to show the separation. Maximum ozone levels
were so high in 2003 that the isoline could not be plotted on the map. The 2011 map
(Figure 3.5.15) shows that a vast area in the north falls below the AAQC even during the
hour in which ozone levels are the highest. According to the MOE (2013a), the reduction
in ozone levels from 2003 to 2011 can be attributed to the progressive reduction in NOx
emissions in Canada and the United States, which also reduces the influence of
transboundary pollution.
33
Figure 3.5.15. 2011 ordinary kriging maximum ozone concentrations
Fine Particulate Matter (PM2.5)
Fine particulate matter, unlike ground-level ozone, does not rely on other chemicals or
meteorological conditions for its creation, and so does not have a season or time of year
in which it is particularly serious. Therefore, dates for which there is the maximum PM2.5
reading are distributed throughout the year, and are not focused in the summer months.
All of the fine particulate matter findings are promising in the context of air quality in
Ontario. Maximum PM2.5, while fluctuating from 2003 to 2007, has experienced a steady
decline since then, and has fallen to under 25 µg/m3, which is not only a large decrease
from the approximately 42.5 µg/m3 it was in 2003, but also falls below the CWS. The
results can be seen in Figure 3.5.16.
34
Maximum PM2.5
50
µg/m3
40
30
20
Trend
10
0
2003
2004
2005
2006
2007 2008
Year
2009
2010
2011
2012
Figure 3.5.16. Maximum fine particulate matter levels trend
A similar trend can be seen when looking at average fine particulate matter. Fluctuations
from 2003 to 2007 can be seen, but since then it has dropped to its lowest point in 2009,
and from then has leveled off at approximately 5 µg/m3, lower than the 7 µg/m3 average
in 2003. The results can be seen in Figure 3.5.17.
µg/m3
Average PM2.5
8
7
6
5
4
3
2
1
0
Trend
2003
2004
2005
2006
2007 2008
Year
2009
2010
Figure 3.5.17. Average fine particulate matter levels trend
35
2011
2012
Perhaps the most encouraging statistic is that of the average number of 24 hour periods in
which the CWS was exceeded. Similar to the other figures, fluctuations can be seen from
2003 to 2007, with the average number of days per station exceeding the CWS reaching
almost 10 in 2005.
Since 2007, however, this number has dropped drastically,
culminating in close to 0 days in 2012 that exceeded the CWS per AQI station. The
results can be seen in Figure 3.5.18.
Average Days per Station
Days Exceeding CWS
10
8
6
4
Trend
2
0
-2
2003
2004
2005
2006
2007 2008
Year
2009
2010
2011
2012
Figure 3.5.18. Average days exceeding the CWS
The ordinary kriging maps for average fine particulate matter levels for each year were
then examined. For the most part, the prediction surfaces slightly underestimated the
variability of predictions for most of the years examined, but slightly overestimated the
variability of predictions for 2003, 2004, and 2009. The statistics can be seen in Table
3.5.3.
36
Table 3.5.3. Ordinary kriging average fine particulate matter statistics
Year
Model
MPE
ASE
2003
Exponential
0.008555585
1.313369575
2004
Spherical
0.070017801
1.228212032
2005
Spherical
0.095736946
1.110966834
2006
Gaussian
0.022354019
0.930343775
2007
Spherical
0.055951884
0.961749278
2008
Spherical
0.053981896
0.914757062
2009
Spherical
0.031358227
0.918704231
2010
Spherical
0.029973126
0.953317665
2011
Spherical
0.056066514
0.929530686
2012
Spherical
0.029819743
0.967414484
SRMSPE
0.929714455
0.995646593
1.049512054
1.003293787
1.014364628
1.02941605
0.974947744
1.044477564
1.003947428
1.033578291
The 2003 PM2.5 kriging prediction map in Figure 3.5.19 shows general north to south
decreasing concentrations, with the exception of the Sault Ste. Marie station, the most
north-westerly. The area east to west from the Golden Horseshoe to the Windsor area has
the highest concentrations, and pockets above 8 µg/m3 are evident near Hamilton and
Windsor.
Figure 3.5.19. 2003 ordinary kriging fine particulate matter concentrations
37
Figure 3.5.20 shows a similar trend to 2003, but with a reduction in predicted PM2.5
concentrations in many areas. As seen in Figure 3.5.17, average levels did drop slightly
from 2003 to 2004, and the map reflects that drop. Most of the northern part of the
prediction surface now falls under 4 µg/m3, a level at which no area was in 2003. Similar
pockets exist around Hamilton and Windsor, though around Hamilton it fell to under 8
µg/m3, while the area around Windsor remained the same.
Figure 3.5.20. 2004 ordinary kriging fine particulate matter concentrations
Figure 3.5.21 represents the year with the highest average PM2.5 concentrations, and
Figure 3.5.17 reflects that statistic. While sharing the similar north-south trend as the
other years, all of the areas fall above 4 µg/m3, as in 2003. Hamilton and the surrounding
area are also similar to 2003, but the concentrations in the Windsor area have increased.
A large portion of the area is now above 9 µg/m3, and a small area exceeds 10 µg/m3.
38
Figure 3.5.21. 2005 ordinary kriging fine particulate matter concentrations
A drastic reduction in fine particulate matter levels across the province can be seen when
examining Figure 3.5.22 as compared to Figure 3.5.21.
The northern part of the
prediction surface is similar, falling between 4 and under 5 µg/m3, but it is the southern
part, especially near Windsor and the United States border that shows much lower
concentrations. No part of that area now falls above 8 µg/m3, whereas in 2005 there was
a small area that fell above 10 µg/m3. As well, a region around Hamilton that had
concentrations between 8 and 9 µg/m3 has fallen to between 6 and 7 µg/m3. The lowest
average fine particulate matter concentrations since 2003 were observed in 2006.
According to the MOE Air Quality in Ontario 2006 Report, this year had the lowest
recorded PM2.5 statistics since it was included in the Smog Alert Program in 2002 (MOE,
2007).
39
Figure 3.5.22. 2006 ordinary kriging fine particulate matter concentrations
Figure 3.5.23 shows similar trends to 2006, but extends in both directions. For example,
the northern part of the study area now shows concentrations between 3 and 4 µg/m3,
where it had been between 4 and 5 µg/m3 previously. Conversely, around the Windsor
area, average concentrations rose to between 8 and 9 µg/m3 where they had been between
7 and 8 µg/m3 in 2006, and the Niagara Falls area also experienced an increase.
40
Figure 3.5.23. 2007 ordinary kriging fine particulate matter concentrations
Figure 3.5.24 shows a similar trend to 2007, but has a larger area in the north that falls in
the lowest prediction interval category. However, a smaller area falls into the highest two
prediction intervals, which is the area surrounding Windsor.
41
Figure 3.5.24. 2008 ordinary kriging fine particulate matter concentrations
Similar trends can again be seen in Figure 3.5.25, but an overall reduction of predicted
fine particulate matter levels is evident. In the north, there is a large area where predicted
PM2.5 levels fall below 3 µg/m3, the first time an area has been in that category in the
study period. At the higher end, the area around Windsor encompassing the highest two
prediction intervals is identical, but now falls between 6 and 8 µg/m3, rather than 7 and 9
µg/m3 in 2008. It can also be seen that 2009 is the year with the lowest average fine
particulate matter levels in the time period studied.
42
Figure 3.5.25. 2009 ordinary kriging fine particulate matter concentrations
Though 2009 was found to have the lowest PM2.5 concentrations for the whole year
averaged over all of the AQI stations, Figure 3.5.26 shows the lowest prediction levels
across the province. Compared to Figure 3.5.25, a similar area falls below 3 µg/m3, but
no areas exceed 7 µg/m3 for the first time in the study period.
43
Figure 3.5.26. 2010 ordinary kriging fine particulate matter concentrations
Figure 3.5.27 shows a slight increase in fine particulate matter from 2011. A small area
near Windsor once again exceeds 7 µg/m3, and there is a smaller area in the north that
falls below 3 µg/m3. Overall the trends are very similar to 2010 and there is no drastic
change in predicted PM2.5 levels.
44
Figure 3.5.27. 2011 ordinary kriging fine particulate matter concentrations
Figure 3.5.28 represents a reduction in predicted fine particulate matter levels since 2011
and reveals one of the lowest concentration prediction surfaces overall. Though the area
in the northern part of the kriging surface below 3 µg/m3 is smaller than the previous
year, the area in the south exceeding 6 µg/m3 is also the smallest in the study period.
45
Figure 3.5.28. 2012 ordinary kriging fine particulate matter concentrations
The maximum fine particulate matter readings were also examined using ordinary kriging
for two years: 2005, the year with the highest maximum levels, and 2012, the year with
the lowest.
Both prediction surfaces slightly underestimated the variability of
predictions, with the SRMSPE being above 1. The statistics can be seen in Table 3.5.4.
Table 3.5.4. Ordinary kriging maximum fine particulate matter statistics
Year
Model
MPE
ASE
SRMSPE
2005
Exponential
-0.047755425
4.2781557
1.083965352
2012
Spherical
0.014626286
4.317073273
1.078006144
Figure 3.5.29 shows high concentrations across the province. Only a small area falls
between 40 and 42 µg/m3, and, similar to the 2003 maximum ozone map in which no
area fell below the AAQC, no area falls below the CWS.
46
Figure 3.5.29. 2005 ordinary kriging maximum fine particulate matter concentrations
Figure 3.5.30 portrays the extreme reduction in maximum fine particulate matter levels
since 2005. The entire prediction surface has concentration levels below the CWS, so it
could not be displayed on the map as it was for ozone. This means that even for the 24
hour periods in which PM2.5 levels were the highest for the entire year, the CWS of 30
µg/m3 was not predicted to be exceeded. Based on Figure 3.5.16, as well as findings
from the MOE (2013a), maximum (and average) PM2.5 concentrations have been steadily
declining since 2003 and have been reduced by approximately 30 percent (MOE, 2013a).
47
Figure 3.5.30. 2012 ordinary kriging maximum fine particulate matter concentrations
3.6 Discussion
Ozone
The results show that, though average ozone levels are fluctuating, they are gradually
increasing. The year with the lowest average ozone levels and with the lowest predicted
surface was 2004, while 2010 was the highest, with 2012 being one of the highest as
well. Conversely, the days with a maximum 1 hour ozone reading above the AAQC are
generally declining, as well as the maximum average ozone levels. This trend has been
identified in the literature; Williams (2008) notes that although peak concentrations have
been declining rapidly, average levels have begun to increase, especially in urban areas.
One noticeable trend when examining the ozone concentrations was that the lowest
predicted concentrations were around large cities, and the concentrations would increase
48
with distance from these cities.
When considering air pollution, this seems
counterintuitive, as it would be expected that major cities would generate the most air
pollution from their larger industrial and automobile presence. This is especially true
when considering that industry and automobile use together account for 89% and 92% of
VOC and NOx emissions respectively, two of the main precursors to ozone. However, it
has been noted in the Ontario Air Quality Report 2011that ozone concentrations are
generally lower in urban areas (MOE, 2013a). This is because ozone concentrations are
reduced when a reaction occurs with NOx. This explains the trend in the ordinary kriging
maps, where Toronto and the surrounding area were almost consistently predicted to have
the lowest concentrations of ozone.
Fine Particulate Matter
Fine particulate matter levels decreased steadily across Ontario from 2003 to 2012. The
years with the highest averages and prediction surfaces were 2003 and 2005, while 2010
and 2012 appeared to have the lowest prediction surfaces province-wide. As well,
average maximum fine particulate matter and 24 hour periods that exceeded the CWS
have also greatly declined since 2003.
In contrast to the findings for ozone, fine particulate matter levels were shown to be the
highest in major population centres and industrial cities. For example, the Windsor area
almost always had the highest predicted concentrations, and the Golden Horseshoe area
near Hamilton was also a hot spot, particularly from 2003 to 2005 (Figures 3.5.19, 3.5.20,
and 3.5.21). These results show that higher PM2.5 concentrations are correlated with high
levels of industrial and anthropogenic activity.
49
One of the reasons for the decline in pollutant concentrations is a result of government
regulations and programs, one of which is Ontario’s Drive Clean program. Drive Clean
is an initiative created by the MOE to reduce vehicle emissions. Vehicle emissions create
over 18 percent of pollution in Ontario that can lead to the creation of smog, and Drive
Clean aims to mitigate those emissions. The program reduces emissions by an annual
average of 36 percent (MOE, 2013b). This is likely a major factor when looking at the
reduction of fine particulate matter levels across Ontario, since approximately a quarter
of all PM2.5 emissions come from automobiles. The program was implemented to reduce
emissions by focusing on proper vehicle maintenance and identifying emissions
problems. Drive Clean acts to improve vehicle fuel consumption and lessen pollutant
release (MOE, 2013b).
The notion of trans-boundary pollution is one of the main things to take into
consideration when examining air pollution in southern Ontario. Both the CCME and
governments in Canada and the United States have agreements concerning transboundary air pollution to mitigate the effects and to lower emissions that could harm both
sides. Therefore, despite government efforts such as Drive Clean, much of the cleanliness
of Ontario’s air is dependent on other sources. This was likely the cause of the air quality
trends experienced, especially when examining fine particulate matter. There was a
general north-south trend of increasing concentrations, likely due to the proximity of the
southern part of Ontario to the United States. As mentioned previously, in additional
findings by Galvez (2007), approximately 60 percent of ozone during smog episodes was
due to anthropogenic emission release from the nearby United States.
50
The distribution of points may also have had an effect on the outcomes of some of the
prediction surfaces. This was mainly an issue for the Sault Ste. Marie station, as it was
fairly remote and distant from the rest of the stations. Therefore, if it had an anomalous
year, its value would have a strong influence on the surrounding area without an AQI
station. The Thunder Bay AQI station was omitted from analysis for this specific reason,
so while the omission of Sault Ste. Marie would result in a less complete analysis, it may
have provided more accurate results for the rest of the area. For example, in Figure
3.5.19, 2003 average PM2.5 concentrations, the Sault Ste. Marie station has a high
concentration. Since the closest AQI station to the east is still quite far, the prediction
surface is heavily based on that reading. The result is relatively high predicted values for
the surrounding area, which may not be an accurate representation of realistic
concentrations. With this in mind, the point density among the 39 stations that were
analyzed could have affected the results. Areas in which there was a high density of
points, such as the Greater Toronto Area, would have produced more accurate results, as
the distance in between the stations is smaller and therefore the predicted readings in
between will be closer to observed measures. In areas where the points are far apart, the
prediction surface can become less accurate since the observed values it relies on are far
away.
For both pollutants, a spike in ambient concentrations was experienced. There is little
literature or information as to why this occurred (the MOE Air Quality Report for 2012
will be released in 2014) but it does coincide with the number of smog alerts. In 2011,
there were only 5 smog advisories lasting a total of 9 days. Comparing this to 2012, there
were 12 smog advisories lasting a total of 30 days (MOE, 2013c). The increase in both
51
particles led to a large increase in the number of smog episodes across Ontario. In terms
of the cause of this increase, it appears that 2012 is an anomalous year; according to 2013
smog statistics up to August 22, 2013, only 1 smog advisory has occurred lasting for a
total of 2 days (MOE, 2013c).
3.7 Conclusion
This analysis provided a spatial investigation of air quality trends in Ontario since 2003.
Ozone and fine particulate matter were targeted, as both are proven detriments to human
and environmental health, and are the two main ingredients of smog. The findings can be
used for environmental organizations and political planning as well, and can corroborate
current air quality reports published in Ontario. The annual air quality report released by
the MOE, for example, only includes readings from 19 of the 40 AQI stations, and does
not include a strong spatial aspect, so a kriging prediction surface can benefit the
statistical analysis of air pollution in Ontario.
It was found that from 2003 to 2012, average ozone concentrations generally increased,
while average fine particulate matter concentrations generally decreased.
However,
maximum levels for each year of each contaminant decreased from 2003 to 2012. The
average amount of times the AAQC for ozone and the CWS for fine particulate matter
was exceeded has also greatly declined since 2003, indicating promising results for air
quality in Ontario.
52
CHAPTER 4: Recommendations and Future Research
One of the caveats of this research project is the notion of trans-boundary air pollution.
As explained in Chapter 2, most of the air pollution in Ontario is not generated in
Ontario, but comes from other sources across the United States border. Thus, while this
analysis shows the trends of air quality in Ontario in recent years, it does not account for
sources of pollution. Therefore, it would be prudent to conduct a further study on
pathways of air to track where the pollution is coming from and attempt to quantify how
much is created in Ontario and how much is from foreign sources.
Another area to be explored for air quality in Ontario is the effect that elevation has on
contaminant readings. Each AQI station has a different height of air intake in metres, as
well as the elevation above sea level in metres. For example, the elevation above sea
level ranges from 55 metres at the Cornwall station to 330 metres at the Guelph station.
The height of air intake also ranges from 3 metres at several stations to 15 metres at the
Chatham station (MOE, 2013a). Therefore, it would be pertinent to examine whether or
not the elevation at which the samples are taken has an effect on the observations.
Other ambient air pollutants could be examined as well to provide a more complete
analysis on the state of air quality. Ozone and fine particulate matter were chosen for this
study since they were measured at each of the 40 AQI stations, which resulted in a more
complete spatial analysis.
However, certain stations across the AQI network also
measure nitrogen dioxide, carbon monoxide, sulphur dioxide, and total reduced sulphur
compounds. Fenger (1999) suggests that sulphur dioxide, nitrogen oxides and carbon
monoxide fall under the category of major pollutants. Therefore, a future study could
53
examine the concentrations of the additional four contaminants to determine their trends
since 2003 for a more complete air quality analysis.
Additionally, different kriging methods could be examined to determine their
effectiveness in modeling ambient air pollutant concentrations. For example, Guo et al.
(2007) used fuzzy membership grade kriging to predict PM10 levels in California.
Similarly Shad et al. (2009) used fuzzy genetic linear membership kriging to examine
PM10 levels in Tehran. Other methods have been used in the literature as well, such as
universal kriging and inverse distance weighting (Beelen et al., 2009; Hooyberghs et al.,
2006; Joseph et al., 2013). Therefore, the same study could be conducted using a different
kriging or spatial interpolation method, and the results could be compared to determine
the best predictor.
While decreasing maximum ozone levels initially looks promising for air quality in
Ontario, it can also mean in increase in NOx, another harmful greenhouse gas. Since its
reaction with ozone reduces overall ozone concentrations, this could mean an increase in
NOx concentrations, which is arguably a more dangerous pollutant. Therefore, research
should be done into the prominence of NOx in Ontario, as well as its relationship to ozone
levels at each measurement.
An additional area to be explored is pollutant concentrations in the United States.
Transboundary pollution has been explained in one context, but pollution also enters the
United States from Canada. Therefore, it would be useful to perform kriging in areas
surrounding Ontario in the United States to compare pollutant concentrations and their
relation to transboundary pollution.
54
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